This release is a pre-release and may not be stable for production use.
Stoner
Scientific data analysis for experimental condensed matter physics.
Stoner helps you load experimental measurements, preserve their metadata, fit physical models, process images and apply the same analysis to collections of files. It originated in the Condensed Matter Physics group at the University of Leeds and builds on NumPy, SciPy, Matplotlib and lmfit.
Start with the user guide, browse the API reference, or explore the example scripts and sample data in the repository.
Working with measurements
The five main classes are available directly from Stoner:
Data: a numerical table with masks, column roles and experimental metadata; includes loading, transformations, plotting and curve fitting.
DataFolder: collections of measurements that can be filtered, grouped and processed together, with access to their combined metadata.
ImageFile: an image with metadata, masks and image-processing methods.
ImageFolder: collections of images with grouping and bulk operations.
ImageStack: images held together in a three-dimensional array for stack operations.
Many analysis methods modify the object in place and return that same object for chaining. Other methods return results, arrays or plotting objects; check individual method documentation. Use data.clone when you need an independent copy before processing.
Registered loaders recognise supported instrument and facility formats. HDF5 and ZIP support lives in Stoner.formats and Stoner.folders; support for specific layouts does not imply that every arbitrary HDF5 file can be read.
Installation
The current source requires Python 3.12 or newer. The test workflow covers Python 3.12-3.14 on Linux and Python 3.14 on macOS. Published package versions may lag behind the source branch; consult the selected release’s requirements.
We recommend a Conda-based Python distribution, such as Anaconda or Miniforge. Create a dedicated environment using the phygbu and conda-forge channels:
conda create -n stoner -c phygbu -c conda-forge python=3.14 stoner conda activate stoner
Alternatively, install the published package into an activated Python virtual environment with pip:
python -m pip install Stoner
Optional features
Core installation does not require OCR, specialist format readers or a Qt GUI. For pip installations, extras select additional functionality, for example:
python -m pip install "Stoner[TDMS,image_alignment,ocr]"
Other extras include facility_formats, hyperspy, plot_styles, PrettyPrint, mimetype_detection, numba, cv2 and qt. Their declarations are in pyproject.toml. In Conda environments, install optional packages from the Conda channels where available.
OCR is optional. The ocr extra installs the Python wrapper pytesseract; text recognition also needs the separate Tesseract executable. For a Conda environment, both can be installed with:
conda install -c conda-forge pytesseract tesseract
A small example
Create a measurement, label its columns and attach experimental metadata:
import numpy as np
from Stoner import Data
data = Data(np.array([[0.0, 1.0], [2.0, 5.0], [1.0, 3.0]]),
column_headers=["Field", "Signal"], setas="xy")
data["Temperature"] = 4.2
ordered = data.clone.sort("Field")
ordered.plot()
For loading real measurements and fitting models, see the user guide and example scripts.
Development and contributions
The maintained source is on the stable branch of the repository. To work on the code with its test dependencies:
git clone --branch stable https://github.com/stonerlab/Stoner-PythonCode.git cd Stoner-PythonCode conda env create -f tests/test-env.yml conda run -n test-environment python -m pip install --no-deps -e . conda run -n test-environment python -m pytest
Read the contributor guide for documentation builds and extension points, AGENTS.md for repository conventions, and the maintenance plan for current work. Report reproducible problems through the issue tracker. Release tags and earlier source are available in the repository.
Contact, licence and citation
The lead developer is Gavin Burnell (g.burnell@leeds.ac.uk), with contributions from current and former members of the Leeds Condensed Matter Physics group. See the user guide for contributor credits.
Copyright University of Leeds and contributors, except where individual files state otherwise. Stoner is licensed under the GNU General Public License v3; see LICENSE.md. Please cite the package using its Zenodo DOI.
1 maintainer
Metadata
1 maintainer
Release files for Stoner 0.12.0a2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| stoner-0.12.0a2.tar.gz | 46.8 MB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| stoner-0.12.0a2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 50.0 MB
Release files / stoner-0.12.0a2.tar.gz
| Download URL | stoner-0.12.0a2.tar.gz |
|---|---|
| Size | 46.8 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
c8b5573f9cc59ee2fc7ebee8a1d0494c49dca3edf6a2a89f5c5fdf0da8800c98
|
|
BLAKE2b-256 checksum How to use checksums |
36657f8b98fb56a442144a3003384f788e6e7d4b6e0e54b4186bfa560d2c9c7d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Release files / stoner-0.12.0a2-py3-none-any.whl
| Download URL | stoner-0.12.0a2-py3-none-any.whl |
|---|---|
| Size | 3.3 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
d0533b179caf41d1ec549a82ccd01ffd72d04d4a506316d562a31c6379a0e468
|
|
BLAKE2b-256 checksum How to use checksums |
49a6aa586d725ac8ae9e1db86aca875c39244b3775295d19cfbb9c96c389036b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Security for Stoner 0.12.0a2
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
